How can caching mechanisms be used in RAG to reduce latency, and what types of data might we cache (embeddings, retrieved results for frequent queries, etc.)?

Caching mechanisms can significantly reduce latency in Retrieval-Augmented Generation (RAG) systems by storing reusable computational outputs, avoiding redundant processing for repeated or similar requests. The core idea is to cache intermediate results—such as embeddings, retrieved documents, or even final generated responses—so that future queries can skip expensive steps like embedding generation or database lookups. This approach is particularly effective for applications with repetitive queries or stable data sources, where cached data remains valid over time.

Three types of data are commonly cached in RAG systems. First, embeddings of queries or documents can be cached to bypass the computational cost of generating them repeatedly. For example, if a user asks, “What is quantum computing?” the system can cache the embedding of this query and reuse it if the same question is asked again. Second, retrieved results from external databases or knowledge bases can be stored. If frequent queries (e.g., “latest Python version”) consistently retrieve the same documents, caching these results avoids redundant searches. Third, generated responses themselves can be cached for identical queries, though this is only viable when answers don’t change frequently (e.g., factual questions). For example, a medical RAG system might cache answers to common symptom-related queries.

When implementing caching, developers must balance speed gains with data freshness. Embedding caches work well when queries have high semantic overlap, but may require techniques like approximate nearest neighbor search to match similar-but-not-identical queries. Retrieved document caches need invalidation strategies if the underlying data changes—for instance, a news-focused RAG system might refresh its cache hourly. Precomputing embeddings for static document collections (e.g., historical research papers) is another optimization. However, over-caching dynamic data (e.g., stock prices) can lead to stale results. Tools like Redis or in-memory dictionaries are often used, with cache keys derived from query hashes or embedding clusters. Testing cache hit rates and tuning eviction policies (e.g., LRU) are critical to maintain efficiency.